Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
ACM SIGMOD Record
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Selecting an Optimal Set of Secondary Indices
Proceedings of the Proceedings of the 1st European Cooperation in Informatics on ECI Conference 1976
COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
To tune or not to tune?: a lightweight physical design alerter
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Physical design refinement: The ‘merge-reduce’ approach
ACM Transactions on Database Systems (TODS)
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Autonomous Management of Soft Indexes
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Self-organizing tuple reconstruction in column-stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Self-selecting, self-tuning, incrementally optimized indexes
Proceedings of the 13th International Conference on Extending Database Technology
Benchmarking adaptive indexing
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
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Physical design represents one of the hardest problems for database management systems. Without proper tuning, systems cannot achieve good performance. Offline indexing creates indexes a priori assuming good workload knowledge and idle time. More recently, online indexing monitors the workload trends and creates or drops indexes online. Adaptive indexing takes another step towards completely automating the tuning process of a database system, by enabling incremental and partial online indexing. The main idea is that physical design changes continuously, adaptively, partially, incrementally and on demand while processing queries as part of the execution operators. As such it brings a plethora of opportunities for rethinking and improving every single corner of database system design. We will analyze the indexing space between offline, online and adaptive indexing through several state of the art indexing techniques, e. g., what-if analysis and soft indexes. We will discuss in detail adaptive indexing techniques such as database cracking, adaptive merging, sideways cracking and various hybrids that try to balance the online tuning overhead with the convergence speed to optimal performance. In addition, we will discuss how various aspects of modern techniques for database architectures, such as vectorization, bulk processing, column-store execution and storage affect adaptive indexing. Finally, we will discuss several open research topics towards fully automomous database kernels.